Project Summary Alzheimer's Disease (AD) is a growing epidemic, and potential treatments are unlikely to be effective unless deployed during the earliest stages of AD, prior to cognitive symptoms. Currently there are no inexpensive, non-invasive biomarkers for effective AD screening necessary for early recognition and treatment on a broad scale. Sleep is abnormal in preclinical AD, even prior to cognitive symptoms, and disrupted sleep may in turn accelerate AD pathological mechanisms. Electroencephalography (EEG) directly measures brain function, and the stereotyped nature of sleep EEG offers a particularly rich opportunity to identify biomarkers of brain dysfunction due to AD. The central hypothesis of the proposed study is that sleep-wake brain mechanisms are abnormal very early in AD, and can be detected via subtle but distinct sleep and EEG changes. The objective is to develop sleep and EEG biomarkers of AD, to enable non-invasive and inexpensive screening on a large scale, through the following specific aims. Aim 1) Identify sleep-wake patterns across the 24-hour day characteristic of AD pathology. Ambulatory sleep-EEG data will be recorded over the 24-hour period in the home setting from a large, diverse, community-based cohort, with the hypothesis that increased sleep-wake transitions over the 24-hour day are characteristic of preclinical-to-mild AD. Aim 2) Assess slow wave integrity measures as biomarkers of AD pathology. EEG abnormalities of slow wave sleep are particularly associated with elevated amyloid-? levels and plaques. Novel analytic techniques will extract bihemispheric slow wave coherence, slow wave velocity, and slow wave intradaily ratio from EEG data collected during sleep and wake. The hypothesis is that amyloid plaques present in early AD will reduce slow wave integrity by all three measures. Aim 3) Determine the EEG signature of AD using machine learning. Machine learning techniques will be applied to EEG from a full attended overnight polysomnogram, to identify a ?signature? of AD pathology. The goal is to identify a ?signature? that can be detected with spatially limited EEG data that could be collected at home. The expected outcome of these aims is to identify sleep-EEG biomarkers of AD that can be detected noninvasively and inexpensively at home. The impact of our work will be the ability to screen large populations easily for AD pathology, so that affected individuals can be identified and treated. Moreover, sleep-EEG biomarkers could be used to track disease progression and treatment response in clinical trials for AD. Lastly, since sleep disturbance has a direct effect on AD pathology, by identifying sleep-EEG changes very early in the pathological process, we may be able to intervene, improve sleep, and potentially change the trajectory of AD. !